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            天地何用？不能席被，风月何用？不能饮食。纤尘何用？万物其中，变化何用？道法自成。面壁何用？不见滔滔，棒喝何用？一头大包。生我何用？不能欢笑，灭我何用，不减狂骄。从何而来？同生世上，齐乐而歌，行遍大道。万里千里，总找不到，不如与我，相逢一笑。芒鞋斗笠千年走，万古长空一朝游，踏歌而行者，物我两忘间。嗨！嗨！嗨！自在逍遥……​——今何在
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                                摘要Prompting 在使大型预训练语言模型 (LM) 执行各种 NLP 任务方面取得了令人印象深刻的成功，尤其是在只有很少下游数据的情况下。然而，自动找到每项任务的最佳提示具有挑战性。大多数现有工作诉诸于调整软提示（例如嵌入），但这些提
                            
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                                摘要NeurIPS 2023
大型语言模型 (LLM) 逐渐被用作各种应用程序的机器学习服务和接口工具。然而，法学硕士的安全影响，特别是与对抗性和特洛伊木马攻击有关的影响，仍未得到充分研究。在本文中，我们提出了 TrojLLM，一种自动黑盒
                            
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                                摘要在本文中，我们首先总结了针对PLM的更具威胁性的后门攻击应满足的要求，然后提出了一种称为UOR的新后门攻击方法，该方法通过将手动选择变为自动优化来打破先前方法的瓶颈。具体来说，我们定义了中毒监督对比学习，它可以自动学习各种 PLM 触发
                            
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                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-11-22
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                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
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                    <a href="/2023/11/16/lun-wen-jing-du-poisoning-language-models-during-instruction-tuning/">
                        <div class="card-image">
                            
                            
                            <img src="/medias/featureimages/0.jpg" class="responsive-img" alt="论文精读——Poisoning Language Models During Instruction Tuning">
                            
                            <span class="card-title">论文精读——Poisoning Language Models During Instruction Tuning</span>
                        </div>
                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                摘要在这项工作中，我们表明对手可以向这些数据集提供有毒示例，从而使他们能够在输入中出现所需的触发短语时操纵模型预测。例如，当下游用户提供的输入提到“乔·拜登”时，中毒的语言模型将难以对该输入进行分类、总结、编辑或翻译。为了构建这些有害示例，
                            
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                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-11-16
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                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
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                    <a href="/2023/10/16/lun-wen-jing-du-notable/">
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                            <img src="/medias/featureimages/11.jpg" class="responsive-img" alt="论文精读——NOTABLE">
                            
                            <span class="card-title">论文精读——NOTABLE</span>
                        </div>
                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                摘要现有针对基于提示的模型的后门攻击考虑将后门注入整个嵌入层或词嵌入向量。此类攻击很容易受到下游任务的再训练和不同的提示策略的影响，从而限制了后门攻击的可转移性。
在这项工作中，我们提出了针对基于提示的模型的可转移后门攻击，称为 NOTAB
                            
                        </div>
                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-10-16
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                            <span class="publish-author">
                                
                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
                            </span>
                        </div>
                    </div>

                    
                    <div class="card-action article-tags">
                        
                        <a href="/tags/%E6%8F%90%E7%A4%BA%E5%AD%A6%E4%B9%A0/">
                            <span class="chip bg-color">提示学习</span>
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                        <a href="/tags/%E5%90%8E%E9%97%A8%E6%94%BB%E5%87%BB/">
                            <span class="chip bg-color">后门攻击</span>
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            <div class="article col s12 m6 l4" data-aos="zoom-in">
                <div class="card">
                    <a href="/2023/09/17/lun-wen-jing-du-backdoor-attacks-on-pre-trained-models-via-poisoned-prompt-tuning/">
                        <div class="card-image">
                            
                            
                            <img src="/medias/featureimages/14.jpg" class="responsive-img" alt="论文精读——Backdoor Attacks on Pre-trained Models via Poisoned Prompt Tuning">
                            
                            <span class="card-title">论文精读——Backdoor Attacks on Pre-trained Models via Poisoned Prompt Tuning</span>
                        </div>
                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                摘要最近，P-Tuing作为一种新的学习范式表现出了卓越的性能，它冻结了预先训练的语言模型（PLM）并仅调整一些软提示。固定的PLM只需要加载不同的提示即可适应不同的下游任务。然而，与PLM相关的提示可能会添加一些恶意行为，例如后门。利用中
                            
                        </div>
                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-09-17
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                            <span class="publish-author">
                                
                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
                            </span>
                        </div>
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            <div class="article col s12 m6 l4" data-aos="zoom-in">
                <div class="card">
                    <a href="/2023/09/08/lun-wen-jing-du-prompt-as-triggers-for-backdoor-attack/">
                        <div class="card-image">
                            
                            
                            <img src="/medias/featureimages/13.jpg" class="responsive-img" alt="论文精读——PROMPT AS TRIGGERS FOR BACKDOOR ATTACK">
                            
                            <span class="card-title">论文精读——PROMPT AS TRIGGERS FOR BACKDOOR ATTACK</span>
                        </div>
                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                摘要基于提示的学习范式弥合了预训练和微调之间的差距，在多项 NLP 任务上实现了最先进的性能，特别是在少数样本设置中。尽管应用广泛，但基于提示的学习很容易受到后门攻击。文本后门攻击旨在通过触发器注入和标签修改来毒害训练样本的子集，从而将目标
                            
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                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-09-08
                            </span>
                            <span class="publish-author">
                                
                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
                            </span>
                        </div>
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                            <span class="chip bg-color">对抗攻击</span>
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            <div class="article col s12 m6 l4" data-aos="zoom-in">
                <div class="card">
                    <a href="/2023/09/07/lun-wen-jing-du-promptbench/">
                        <div class="card-image">
                            
                            
                            <img src="/medias/featureimages/19.jpg" class="responsive-img" alt="PromptBench Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts">
                            
                            <span class="card-title">PromptBench Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts</span>
                        </div>
                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                摘要学术界和工业界对大型语言模型( Large Language Models，LLMs )的依赖程度越来越高，因此有必要全面了解LLMs对提示的鲁棒性。针对这一重要需求，我们引入了PromptBench这一鲁棒性基准来衡量LLMs对对抗提
                            
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                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-09-07
                            </span>
                            <span class="publish-author">
                                
                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
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                    <a href="/2023/08/01/lun-wen-jing-du-exploring-the-universal-vulnerability-of-prompt-based-learning-paradigm/">
                        <div class="card-image">
                            
                            
                            <img src="/medias/featureimages/18.jpg" class="responsive-img" alt="论文精读——Exploring the Universal Vulnerability of Prompt-based Learning Paradigm">
                            
                            <span class="card-title">论文精读——Exploring the Universal Vulnerability of Prompt-based Learning Paradigm</span>
                        </div>
                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                
摘要基于提示的学习范式弥合了预训练和微调之间的差距，并且在少数样本设置下有效地工作。然而，我们发现这种学习范式继承了预训练阶段的漏洞，在该阶段，模型预测可能会通过在文本中插入某些触发器而被误导。在本文中，我们通过注入后门触发器或仅使用纯文
                            
                        </div>
                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-08-01
                            </span>
                            <span class="publish-author">
                                
                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
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                            <span class="chip bg-color">对抗攻击</span>
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                    <a href="/2023/07/29/lun-wen-jing-du-badprompt-backdoor-attacks-on-continuous-prompts/">
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                            <img src="/medias/featureimages/0.jpg" class="responsive-img" alt="论文精读——BadPrompt Backdoor Attacks on Continuous Prompts">
                            
                            <span class="card-title">论文精读——BadPrompt Backdoor Attacks on Continuous Prompts</span>
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                    </a>

                    <div class="card-content article-content">
                        <div class="summary block-with-text">
                            
                                摘要基于提示的学习范式最近引起了很多研究关注。在引导下游任务时，很少有研究研究基于提示的模型的安全问题。在本文中，我们首次研究了连续提示学习算法对后门攻击的脆弱性。
为了应对这一挑战，我们提出了 BadPrompt，一种轻量级的任务自适应算
                            
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                        <div class="publish-info">
                            <span class="publish-date">
                                <i class="far fa-clock fa-fw icon-date"></i>2023-07-29
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                                <i class="fas fa-user fa-fw"></i>
                                J Sir
                                
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